Rapid quantification of COVID-19 pneumonia burden from computed tomography with convolutional long short-term memory networks

PURPOSE: Quantitative lung measures derived from computed tomography (CT) have been demonstrated to improve prognostication in coronavirus disease 2019 (COVID-19) patients but are not part of clinical routine because the required manual segmentation of lung lesions is prohibitively time consuming. W...

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Detalles Bibliográficos
Autores principales: Killekar, Aditya, Grodecki, Kajetan, Lin, Andrew, Cadet, Sebastien, McElhinney, Priscilla, Razipour, Aryabod, Chan, Cato, Pressman, Barry D., Julien, Peter, Chen, Peter, Simon, Judit, Maurovich-Horvat, Pal, Gaibazzi, Nicola, Thakur, Udit, Mancini, Elisabetta, Agalbato, Cecilia, Munechika, Jiro, Matsumoto, Hidenari, Menè, Roberto, Parati, Gianfranco, Cernigliaro, Franco, Nerlekar, Nitesh, Torlasco, Camilla, Pontone, Gianluca, Dey, Damini, Slomka, Piotr
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Society of Photo-Optical Instrumentation Engineers 2022
Materias:
Image Processing
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9446878/
https://www.ncbi.nlm.nih.gov/pubmed/36090960
http://dx.doi.org/10.1117/1.JMI.9.5.054001

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9446878/
https://www.ncbi.nlm.nih.gov/pubmed/36090960
http://dx.doi.org/10.1117/1.JMI.9.5.054001

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